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# Senior Architect - Data Processing Systems, Analytics and Data Intelligence

- **Company:** NVIDIA Corporation
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $49,920.0 - $56,160.0
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Data Analysis, Systems Engineering, Nvidia CUDA, Databases, Computer Engineering, Computer Graphics, Extract Transform Load (ETL), Microprocessors, Distributed Data Store, Network Topologies, Systems Analysis, Data Intelligence, Open Source Technology, Performance Tuning, Software Architecture, Search Technologies, Data Processing, Graphics Processing Unit (GPU), Storage Technologies, Information Technology
- **Published:** October 17, 2026
- **Apply:** https://www.jofdav.com/jobs/60156905-senior-architect-data-processing-systems-analytics-and-data-intelligence

## About the Role

* MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field, or equivalent experience.
 * 8+ years of experience in distributed data platforms, databases, systems engineering, performance architecture, accelerated computing, or HW/SW co-design.
 * Extensive knowledge in one or more of these areas: data processing systems (databases, query engines, or vector search), accelerated computing, high-performance IO, CPU/GPU architecture, or large-scale performance analysis.
 * Experience evaluating system-level trade-offs: compute, IO provisioning, memory subsystem development, network topology, and storage tiering across diverse workloads a strong plus.
 * Ability to translate sophisticated system analysis into clear, actionable recommendations for silicon architects, software engineers, and executive leadership.
 * Ability to work across matrixed organizations to drive alignment and deliver results.

Ways to stand out from the crowd:

 * Hands-on experience with GPU-accelerated data processing (cuDF, RAPIDS, cuVS, CUDA).
 * Deep familiarity with structured data engines and/or vector database systems.
 * Experience with mathematical optimization solvers and understanding of optimization fundamentals.
 * Background in network/storage architecture.

## Description

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology-and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAN, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

Are you ready to build the future of data processing with NVIDIA? As a Senior Architect in Data Processing Systems for Analytics and Data Intelligence (ADI), you'll define and improve hardware and software architectures for diverse set of data processing workloads. Join us in our mission to deliver outstanding performance at scale, using our accelerated data processing platform that combines CPUs, GPUs, high-bandwidth networking, and more!

What you'll be doing:

 * Co-design future architectures for NVIDIA's data processing platforms, targeting performance, efficiency, and cost.
 * Engage deeply with end users, open-source communities, ISVs, storage partners, and CSPs to improve their experience by incorporating customer insights into roadmaps and reference architectures.
 * Drive full-stack performance optimization, from CUDA kernels and accelerated libraries to system-level IO, shuffle, compression, and data movement.
 * Collaborate with CPU, GPU, and platform architects to shape hardware features using workload analysis for hardware-software co-design.
 * Partner with internal teams to build and apply constraint-based frameworks and optimization solvers to navigate complex design trade-offs across competing metrics.
 * Develop benchmarking frameworks to evaluate performance sensitivities and guide hardware-software co-optimization
 * Analyze production workloads throughout the data processing landscape to identify compute motifs, IO patterns, and optimization opportunities across the full platform stack.